Building the Unpredictable: How to Craft a Successful AI Business Case in the Real World
Imagine sitting down with the top leaders of a company to pitch the next big AI initiative. Excitement is in the air, but the biggest hurdle? Convincing everyone of the uncertain journey ahead without a clear map to navigate by. This is often the reality when building an AI business case. I recently surveyed several Danish CIOs about their AI endeavors, and unsurprisingly, one of the most significant challenges they face is crafting a robust AI business case. However, with the right strategies, you can turn this daunting task into an achievable goal.
Why is Crafting an AI Business Case So Challenging?
Unlike traditional IT projects, where you can predict costs, timelines, and outcomes with relative accuracy, AI projects are inherently experimental. This lack of predictability stems from several factors:
- The amount and quality of data required.
- The selection of the right algorithmic approach.
- The results which can vary significantly based on subtle differences in data and problem context.
Because of these uncertainties, building an AI business case requires a shift in mindset and approach. Let’s dive into how you can effectively manage this process.
The Cost Side: Controlling Rather than Predicting
Traditional methods of cost prediction don’t work well with AI projects. Instead, the focus should be on cost control. Here are some key strategies to help manage costs effectively:
Iterative Approach
Adopting an agile methodology allows for iterative learning. Each iteration provides valuable insights, making the next steps clearer and more defined:
- Understand the data: How much effort does it take to collect and prepare? How much data is needed?
- Gauge user reactions: How do users respond to the AI’s quality?
- Test achievable quality: Is the initial quality close to acceptable?
Milestone Funding
Milestone funding helps in releasing funds only when specific criteria are met, ensuring that costs are controlled at every stage:
Collect Data: The first milestone should focus on data collection, ensuring data is of a certain quality and quantity within budget. Frequent data updates should also be considered.
Build Models: The second milestone involves building and iterating on models. This step should include initial deployments to test environments to gauge feasibility.
Deployment: Finally, deploying AI models involves integrating them into a production or test environment. This often includes handling large data streams and potential user behavior complexities.
Bundling AI Projects
Combining several AI initiatives into one business case allows you to balance the risk of individual failures with the potential success of other projects. This holistic approach helps in making the overall venture more resilient to unforeseen challenges.
AI Business Culture: Embracing the Experimental
Creating an AI-friendly business culture is crucial. This involves:
- Viewing AI project failures as valuable null-results, offering a clear direction on what does not work.
- Promoting a culture that focuses on cost control and experimentation rather than rigid cost prediction.
Management plays a crucial role in fostering this culture, making it possible for these innovative approaches to thrive.
The Revenue Side: Managing Expectations
One of the toughest questions to answer is, “How good will the AI be?” The right response is often, “I don’t know.” Revenue projections tied directly to AI quality are challenging to predict. Here are some tips to manage revenue expectations:
Set Realistic Goals
Focus on creating a business case for a “good enough” AI rather than a perfect one. Be clear and specific about what the AI should achieve, keeping in mind that new technologies often carry overly ambitious expectations.
Presenting Your Business Case
Once your business case is ready, the final hurdle is often presenting it to stakeholders who are used to traditional IT paradigms. Begin by elucidating the core principles of AI and why they necessitate a different approach. Gaining buy-in for these principles ensures smoother sailing for your AI project proposals.
By adopting these strategies and fostering the right culture, crafting a successful AI business case becomes not just possible but a structured, manageable process. The world of AI is ripe with potential; it just requires a new lens to seize the opportunities effectively.
What challenges have you faced in building AI business cases? Share your insights and experiences in the comments below!